How an AI expert took on his toughest project ever: writing code to save his son’s life
BIRMINGHAM, Ala. — Cristina Might drew close to her son. He was listless and groggy after weeks of battling a puzzling illness that had filled his lungs with fluid and, hours earlier, stopped his breathing entirely. A code team had rushed to Buddy’s bedside and jolted him back to life, but now the 11-year-old with the broad smile was gray, his eyes unable to focus. His mom leaned nearer still. It was time to say goodbye.
But Cristina’s words to her son, a brown-eyed boy who loved dolphins and his aquarium, offered no hint of her desperation: “I was telling him it was all going to be OK, that his fishies couldn’t wait to see him again and that he had to hurry up and come home.”
Somehow, Buddy made it through that night this past May, allowing doctors at Children’s Hospital of Alabama to insert a tube to drain his lungs. His illness had caused a frightening cascade of symptoms: a yellowish substance in his bones and a bulging abdomen, on top of the deluge of fluid. After weeks in and out of the hospital, no one had come up with a diagnosis, but the drainage tube would hopefully give them a few more days to find an answer.
Frustrated by the lack of progress, Matt Might, Buddy’s father and a computer programmer who heads the Hugh Kaul Precision Medicine Institute at the University of Alabama at Birmingham (UAB), began attacking the problem the only way he knew how — by writing code.
His work at the institute involves creating an artificial intelligence system capable of sifting and analyzing vast stores of biomedical information. Sitting at Buddy’s bedside, he started building software to query the system for clues about what was causing his son’s symptoms. It was a bit of a Hail Mary. The AI, dubbed mediKanren, is still experimental and used only by a small group of researchers at UAB.
It was developed as part of an ambitious project funded by the National Institutes of Health to link and make searchable decades worth of biomedical data collected by universities and research labs on genes, proteins, disease symptoms, patient outcomes, drugs, and more. This information
